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Record W2062802970 · doi:10.1209/0295-5075/92/40003

Efficient computation of lattice Green's functions for models with nearest-neighbour hopping

2010· article· en· W2062802970 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEurophysics Letters (EPL) · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLattice (music)ComputationUnit cubeHexagonal latticeAtomic orbitalPhysicsNearest neighbourCondensed matter physicsStatistical physicsMathematicsCombinatoricsComputer scienceElectronQuantum mechanicsAlgorithm

Abstract

fetched live from OpenAlex

We show that for models with nearest-neighbour (nn) hopping, the lattice Green's functions can be calculated without the need to perform integrals. Our method applies to rectangular, triangular and honeycomb lattices in two dimensions, and to simple, face-centered and body-centered lattices in three dimensions. External magnetic fields can be dealt with trivially. As an example, we show that our method works for any ratio ϕ/ϕ0 of the magnetic flux through the unit cell, i.e. irrespective of the change in the size of the magnetic unit cell. Other straightforward generalizations are to models with multiple orbitals per site, with any spin-orbit coupling, on-site disorder, and any combinations thereof. The method works equally well in the presence of surfaces. In all cases, accurate values for large distances can be obtained very efficiently and without finite-size effects. The relationship to other computational methods is also analyzed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.213
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it